Ultra-low Energy Consumption Research Articles

Wearable variable-emittance devices—The future of dynamic personal thermoregulation

Using infrared electrochromism as the strategy to combat the fluctuation of environmental conditions, wearable variable-emittance (WeaVE) devices are able to integrate the functionality of personal thermoregulation and closed-loop control into the future textile, featuring its large tunable range, ultra-low energy consumption, lightweight, and wearability. Recently, this new wearable technology has evolved beyond planar electrochromic cells and is moving closer to woven textiles. To further improve electrochromic performance and wearability, comprehensive progress is necessary from materials science to fabrication techniques. In this Perspective, we elaborate on the mechanisms behind electrochemically active WeaVE devices, analyze how dynamic and fundamental studies may improve the electrochromic performance, and explore the possibility of incorporating nanophotonic designs in the development of this future smart textile through research.

Self-Powered Infrared-Detectable BP/Ta2NiS5 Heterojunction and Its Application in Energy-Efficient Optoelectronic Synapses.

The development of energy-efficient and high-performance optoelectronic devices is crucial for the advancement of modern optoelectronic and microelectronic systems. Although the self-powered devices and optoelectronic synapses based on 2D heterojunction show great application prospects, the high energy consumption and infrared band detection of self-powered optoelectronic synapses are still an urgent problem to be solved. In this report, a BP/Ta2NiS5 heterojunction is constructed to achieve infrared detection by leveraging differences in Fermi energy levels. This heterojunction exhibits a high specific detectivity of 6.57×1010, 2.6×1010, and 1.12×1010 Jones and responsivity of 20, 10.6, and 5.9mAW-1 for 1064, 1550, and 2200nm infrared light at 0 bias voltage, respectively. In addition, under the 2200nm light, by applying an ultra-low bias voltage of 800µV, the heterojunction exhibits ultra-low power and energy consumption of 28.8pW and 0.64pJ, successfully simulates a variety of synaptic behaviors under infrared light, and demonstrates its image perception and image memory capabilities. These findings position the BP/Ta2NiS5 heterojunction as an ideal candidate for a multifunctional optoelectronic device crucial for advanced photodetection, neuromorphic computing, and artificial intelligence.

Robust Electric-Field Control of Colossal Exchange Bias in CrI3 Homotrilayer.

Controlling exchange bias (EB) by electric fields is crucial for next-generation magnetic random access memories and spintronics with ultralow energy consumption and ultrahigh speed. Multiferroic heterostructures have been traditionally used to electrically control EB and interfacial ferromagnetism through weak/indirect coupling between ferromagnetic and ferroelectric films. However, three major bottlenecks (lattice mismatch, interface defects, and weak/indirect coupling in multiferroic heterostructures) remain, resulting in only a few tens of milli-tesla EB field. Here, this study reports a robust electric-field control recipe to dynamically tailor the EB effect in a pure CrI3 homotrilayer on a ferroelectric Y-doped HfO2 (HYO) substrate, and demonstrate a colossal and tunable EB field (HE) from -0.15 to +0.33 T, giving rise to an EB modulation of 0.48 T. The charge doping due to ferroelectric HYO film divides a homo-configuration of CrI3 homotrilayer into one antiferromagnetic (AFM) bilayer CrI3 and one ferromagnetic (FM) monolayer CrI3, favoring direct exchange coupling. The synergies of charge doping and electric field induce a transition of magnetic orders from AFM to FM phase in bilayer CrI3, which is also supported by first-principles calculations, leading to the robust electric control of colossal EB effect. The results therefore open numerous opportunities for exploring 2D spintronics, memories, and braininspired in-memory computing.

A Self-Driven Ga2O3 Memristor Synapse for Humanoid Robot Learning.

In recent years, the rapid development of brain-inspired neuromorphic systems has created an imperative demand for artificial photonic synapses that operate with low power consumption. In this study, a self-driven memristor synapse based on gallium oxide (Ga2O3) nanowires is proposed and demonstrated successfully. This memristor synapse is capable of emulating a range of functionalities of biological synapses when exposed to 255nm light stimulation. These functionalities encompass peak time-dependent plasticity, pulse facilitation, and memory learning capabilities. It exhibits an ultrahigh paired-pulse facilitation index of 158, indicating exceptional learning performance. The transition from short-term memory to long-term memory can be attributed to the remarkable relearning capabilities. Furthermore, the potential applications of the memristor synapse is showcased through the successful manipulation of a humanoid intelligent robot. Upon establishing artificial intelligence (AI) systems, the control commands originating from the synaptic device can drive the humanoid robot to perform various actions. Based on the memristor synapses, the autonomous feedback system of the humanoid robot facilitates a good collaboration between robotic actions and bio-inspired light perception. Therefore, this research opens up an effective way to advance the development of neuromorphic computing technologies, AI systems, and intelligent robots that demand ultra-low energy consumption.

Ultralow Energy Consumption and Fast Neuromorphic Computing Based on La0.1Bi0.9FeO3 Ferroelectric Tunnel Junctions.

Low-power and fast artificial neural network devices represent the direction in developing analogue neural networks. Here, an ultralow power consumption (0.8 fJ) and rapid (100 ns) La0.1Bi0.9FeO3/La0.7Sr0.3MnO3 ferroelectric tunnel junction artificial synapse has been developed to emulate the biological neural networks. The visual memory and forgetting functionalities have been emulated based on long-term potentiation and depression with good linearity. Moreover, with a single device, logical operations of "AND" and "OR" are implemented, and an artificial neural network was constructed with a recognition accuracy of 96%. Especially for noisy data sets, the recognition speed is faster after preprocessing by the device in the present work. This sets the stage for highly reliable and repeatable unsupervised learning.

Comprehensively Modulated Sub-Attojoule Operated Optoelectronic Synapses for Image Encryption and Inpainting.

High-performance optoelectronic synaptic transistors play a crucial role in developing and emulating artificial visual systems. However, due to the predominant use of single-structure material modulation in optimizing optoelectronic synapses, their energy consumption significantly trails behind that of electronic synapses by several orders of magnitude. Herein, polymer dielectric layers and optimized contact strategies are adopted to realize the ultralow consumption optoelectronic synapses. Integration of polyimide dielectric significantly enhances photogenerated charge carrier dissociation, leading to substantial improvements in photoresponsivity (1.5 × 106 A·W-1), photodetectivity (6.9 × 1012 Jones), and external quantum efficiency (4.0 × 108%). Additionally, optimized contact properties augment their appeal for ultralow energy consumption in optoelectronic synapse applications. Excitatory postsynaptic current is triggered at an incredibly low voltage of 5 μV and boosts an impressively low energy consumption of 0.05 aJ, ranking among the best-reported results in this field. Next, we demonstrate an integrated system combining the MoS2 optoelectronic synapses with a recurrent neural network enabling 100% accurate recognition of optical signals, particularly in scenarios with aJ-leveled energy consumption. Finally, an image encryption system has been developed, in which images are encrypted by photoelectronic conversion of synapse arrays with random voltage settings and decrypted according to the recurrent neural network-based accuracy. More importantly, once partially damaged images are encrypted, through the decryption image inpainting can be realized due to the high accuracy. The proposed innovative approach holds promise for advancing artificial intelligence applications with improved energy efficiency, information security, and computational capabilities.

A physical derivation of high-flux ion transport in biological channel via quantum ion coherence

Biological ion channels usually conduct the high-flux transport of 107 ~ 108 ions·s−1; however, the underlying mechanism is still lacking. Here, by applying the KcsA potassium channel as a typical example, and performing multitimescale molecular dynamics simulations, we demonstrate that there is coherence of the K+ ions confined in biological channels, which determines transport. The coherent oscillation state of confined K+ ions with a nanosecond-level lifetime in the channel dominates each transport event, serving as the physical basis for the high flux of ~108 ions∙s−1. The coherent transfer of confined K+ ions only takes several picoseconds and has no perturbation effect on the ion coherence, acting as the directional key of transport. Such ion coherence is allowed by quantum mechanics. An increase in the coherence can significantly enhance the ion conductance. These findings provide a potential explanation from the perspective of coherence for the high-flux ion transport with ultralow energy consumption of biological channels.

Organic synaptic transistor showing ultralow energy consumption with a microscale channel by laser ablation

Low energy consumption per synaptic event is important for artificial synapses in applications of highly integrated and large-scale neuromorphic computing systems. Reducing the channel length of a synaptic transistor is an effective method to achieve this goal because such devices can work under low operating voltage and current. In this Letter, we use femtosecond laser ablation to fabricate a microscale slit in an Ag film as the channel of an organic synaptic transistor to obtain low energy consumption. The length of the shortest channel is only 1.6 μm. As a result, the device could be driven by a 50 μV drain bias voltage while output 855 pA excitatory postsynaptic current under a gate spike of 50 mV and 30 ms. The calculated energy consumption per synaptic event is 1.28 fJ, which is comparable to that of a biological synapse (1–10 fJ per synaptic event). Femtosecond laser ablation has been demonstrated a rapid and effective process for the fabrication of microscale channel with high resolution for synaptic transistor, showing large potential for the development of neuromorphic electronics.

Integration of Fano resonances with inverse-designed power splitter

Computer-aided intelligent algorithms are beneficial to realize miniaturized devices with ultra-low energy consumption. In this paper, a single-channel Fano structure is designed and the maximum peak-to-valley contrast 0.973 is achieved. A power splitter with the size of 1 μm × 3 μm is proposed by using inverse design method based on topology optimization, whose average transmittance for each port is about 0.489 within the wavelength range of 1450–1650 nm. Finally, the double-channel outputs with the same and different Fano resonances are achieved by integrating with the optimized Fano structures. Our study is conducive to realize efficient and multi-functional photonic integrated circuits.

Sustainable architectural practices: Integrating green design, smart technologies, and ultra-low energy concepts

This comprehensive study delves into the integration of green building practices, smart technologies, and ultra-low energy consumption strategies in modern architecture. Through a meticulous examination of existing green buildings, the implementation of smart technology, and the achievements of ultra-low energy structures, this paper highlights the pivotal role these practices play in promoting environmental sustainability and operational efficiency. By analyzing life cycle assessments, energy efficiency models, and the application of renewable energy sources, we provide a quantitative and qualitative overview of the benefits and challenges associated with sustainable architectural practices. The analysis reveals significant environmental impacts, including reduced carbon emissions and lower water usage, alongside notable economic benefits such as decreased operational costs and enhanced property values. Furthermore, the paper explores the technological integration of smart systems, emphasizing the importance of innovative solutions for optimizing building performance. Through detailed case studies and real-world applications, we demonstrate the feasibility and effectiveness of these integrated approaches in achieving sustainable, efficient, and comfortable living and working environments. This study not only underscores the necessity of adopting sustainable practices in architecture but also provides a roadmap for future developments in the field.

Fast and broadband spatial-photoresistance modulation in graphene–silicon heterojunctions

Abstract Different types of devices with modulable resistance are attractive for the significant potential applications such as sensors, information storage, computation, etc. Although extensive research has been reported on resistance effects, there is still a need for exploring new mechanisms that offer advantages of low power consumption, high sensitivity, and long-term stability. Here, we report a graphene–Si based spatial-dependence photo-rheostat (SDPR), which enables bipolar resistance modulation in the range of 5 mm with a resistance sensitivity exceeding 1,000 Ω/mm at operating wavelengths from visible to near infrared band (1,550 nm). Especially, at ultra-low energy consumption, the device can achieve modulation of even 5 orders of magnitude of resistance and response speed up to 10 kHz. A theoretical model based on carrier dynamics is established to reveal the diffusion and drift of carriers as a mechanism explaining such experimental phenomenon. This work provides a new avenue to modulate resistance at low power consumption as novel opto-potentiometers in various photoelectric applications.

Skin-like n-type stretchable synaptic transistors with low energy consumption and highly reliable plasticity for brain-inspired computing

Artificial synaptic devices with high transconductance, low energy consumption and good stretchability are important for efficient and energy-friendly neuromorphic computations in the field of bionics. Here, we propose a stretchable substrate inspired by wrinkled surface of human skin, and achieve a low energy operation stretchable synaptic transistor (SST) based on n-type oxide semiconductor. The SSTs exhibit excellent performance, including high mobility (4.08 cm2V−1s−1) and high transconductance (over 12 mS) under ultra-low operation voltage of 0.1 V. They can stand against multi-directional stretching of 10 % and up to 400 stretch/release cycles. In addition, the n-type SSTs achieve synaptic performance at ultra-low energy consumption (0.36 fJ) with dual-mode operation characteristics of excitatory and inhibitory behaviors. Under tensile strain, they exhibit typical synaptic behavior, including short-term/long-term plasticity, pair pulse facilitation, spike voltage/frequency/duration/number-dependent plasticity. More importantly, the SSTs exhibit excellent “learning-forgetting-relearning” feature and good stability under potentiation-depression cycle test, showcasing tremendous potential in brain-inspired computation. Finally, a high recognition accuracy (89.6 %) is attained simulated by handwritten digital datasets. To the best of our knowledge, this is the first stretchable synaptic transistor with oxide semiconductors, and it is also a major breakthrough in n-type stretchable synaptic transistors for high-speed and low-energy calculation and storage for brain-inspired computing.

A novel intercooling carbon dioxide capture process using ionic liquids with ultra-low energy consumption

This study focuses on the use of ionic liquids as solvents for carbon dioxide capture due to their low energy demand. We screened an ideal ionic liquid, 1-ethyl-3-methylimidazolium bis[trifluoromethylsulfonyl]imide ([emim][Tf2N]), based on its excellent absorption capacity and low viscosity. We performed thermodynamic modeling of the gas-liquid phase equilibrium to lay the foundation for process simulation. A basic process was designed for benchmarking performance, and various cases were compared by adjusting key operating parameters such as pressure and temperature. The results showed that the lowest specific capture energy achieved was 1.12 GJ/t at 20 bar and 5 °C. However, further improvements in energy performance were constrained by reduced absorption capacity and increased viscosity resulting from lower operating pressures and temperatures. To address these constraints, we proposed a novel process that optimized the temperature profile in the absorption column by dividing it into two sections with intercooling. This approach did not require significant changes to the base case but offered multidimensional benefits, including reduced energy demand and associated equipment costs. The specific capture energy was further reduced to 1.04 GJ/t. Economic analysis indicated that the capture cost at a production scale of 13.7 kt/a was 206 $/t but had the potential to be reduced to 82 $/t. The costs associated with compressors were found to be the major contributors to both equipment and capture costs. The proposed novel process demonstrates promising benefits and highlights the importance of further investigation into low-pressure operation.

Heterogeneous ion-modulated 2D-WS2 heterosynaptic memtransistor for controllable synaptic modulation and energy-efficient neuromorphic computing

Heterosynaptic plasticity plays a key role in addressing the interactions between local and global neuromodulation and the implementation of more complex biological functions. However, conventional heterosynaptic devices suffer from high energy consumption, high operating voltages and a lack of multi-modal regulation, which hinder the development of next-generation neuromorphic computing. Here, we report a heterogeneous ion-modulated 2D-WS2 four-terminal heterosynaptic memtransistor (Na+@WSHMT) with typical heterosynaptic plasticity at low stimulation voltage (200 mV) and ultra-low energy consumption (150 aJ). Moreover, the potential of four-terminal heterosynaptic memtransistors is demonstrated for enhancing the digit recognition with an increased recognition accuracy from 90.5 % to 93.3 % by stimulating the heterosynaptic terminal (light). Furthermore, convolutional image processing such as feature extraction is successfully performed by a convolutional neural network based on the multi-conductance characteristic of Na+@WSHMT. The ion-modulated heterosynaptic memtransistor provide a promising strategy for energy-efficient and controllable neuromorphic computing.

Highly Sensitive, Low-Energy-Consumption Biomimetic Olfactory Synaptic Transistors Based on the Aggregation of the Semiconductor Films.

Artificial olfactory synaptic devices with low energy consumption and low detection limits are important for the further development of neuromorphic computing and intelligent robotics. In this work, an ultralow energy consumption and low detection limit imitation olfactory synaptic device based on organic field-effect transistors (OFETs) was prepared. The aggregation state of poly(diketopyrrolopyrrole-selenophene) (PTDPP) semiconductor films is modulated by adding unfavorable solvents and annealing treatments to obtain excellent charge transfer and gas synaptic properties. The regulated OFET device can execute basic biological synaptic functions, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term to long-term plasticity, at an ultralow operating voltage of -0.0005 V. The ultralow energy consumption during the biomimetic simulation is in the range of 8.94-88 fJ per spike. Noteworthily, the gas detection limit of the device is as low as 50 ppb, well below normal human NO2 gas perception limits (100-1000 ppb). Additionally, high-pass filtering, Pavlovian conditioned reflexes, and decoding of "Morse code" were simulated. Finally, a grid-free conformal device with outstanding flexibility and stability was fabricated. In conclusion, the control of semiconductor thin-film aggregation provides effective guidance for preparing low-energy-consumption, highly sensitive olfactory nerve-mimicking devices and promoting the development of wearable electronics.

Nanolasers

Abstract As the demand for smaller and more compact lasers increases, the physical dimensions of laser diodes are already at the diffraction limit, which impairs this miniaturization trend and limits direct laser integration into photonic and especially nanophotonic circuits. However, plasmonics has allowed the development of a novel class of lasers that can be manufactured without being limited by diffraction, exhibiting ultralow energy consumption, small volumes, and high modulation speeds that could someday compete with their modern macroscale counterparts. Nevertheless, a wide variety of issues create roadblocks for further development and commercial adoption. Here we conduct a monolithic review in which we formulate the definition of a nanolaser, categorize nanolasers, and examine their properties and applications to determine if nanolasers do present a potential technological revolution as they seem to exhibit or are too restricted by the issues that plague them to ever succeed.

Strain-insensitive viscoelastic perovskite film for intrinsically stretchable neuromorphic vision-adaptive transistors

Stretchable neuromorphic optoelectronics present tantalizing opportunities for intelligent vision applications that necessitate high spatial resolution and multimodal interaction. Existing neuromorphic devices are either stretchable but not reconcilable with multifunctionality, or discrete but with low-end neurological function and limited flexibility. Herein, we propose a defect-tunable viscoelastic perovskite film that is assembled into strain-insensitive quasi-continuous microsphere morphologies for intrinsically stretchable neuromorphic vision-adaptive transistors. The resulting device achieves trichromatic photoadaptation and a rapid adaptive speed (<150 s) beyond human eyes (3 ~ 30 min) even under 100% mechanical strain. When acted as an artificial synapse, the device can operate at an ultra-low energy consumption (15 aJ) (far below the human brain of 1 ~ 10 fJ) with a high paired-pulse facilitation index of 270% (one of the best figures of merit in stretchable synaptic phototransistors). Furthermore, adaptive optical imaging is achieved by the strain-insensitive perovskite films, accelerating the implementation of next-generation neuromorphic vision systems.

Flexible and Energy-Efficient Synaptic Transistor with Quasi-Linear Weight Update Protocol by Inkjet Printing of Orientated Polar-Electret/High-k Oxide Composite Dielectric.

Inkjet printing artificial synapse is cost-effective but challenging in emulating synaptic dynamics with a sufficient number of effective weight states under ultralow voltage spiking operation. A synaptic transistor gated by inkjet-printed composite dielectric of polar-electret polyvinylpyrrolidone (PVP) and high-k zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear weight update with a large variation margin is obtained through the coupling effect and the facilitation of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows. Crucial features of biological synapses including long-term plasticity, spike-timing-dependence-plasticity (STDP), "Learning-Experience" behavior, and ultralow energy consumption (<10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1%) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.

Spin-wave-driven tornado-like dynamics of three-dimensional topological magnetic textures

The abundant topological magnetic textures in three-dimensional systems provide opportunities to investigate the fundamental spin dynamics and realize spintronic applications. The dynamics of such magnetic textures have however rarely been studied, especially for those driven by spin waves, which allow applications with ultralow energy consumption and ease of implementation even in insulating systems. Here, we report our micromagnetic simulations on the spin-wave-driven dynamics of a skyrmion tube (SkT) and chiral bobber (ChB) in a thick magnetic film. We predict tornado-like dynamics in both SkT and ChB, where the topological centers present a lateral rotation with the rotation centers forming a distorted profile in the thickness direction. While the velocity of SkT scales with the driving power, the ChB motion presents a threshold in the driving field, which is found to depend linearly on its penetration length. This distinct behavior could be useful to differentiate ChB from SkT and estimate its penetration length experimentally.

One memristor–one electrolyte-gated transistor-based high energy-efficient dropout neuronal units

Abstract Artificial neural networks (ANN) have been extensively researched due to their significant energy-saving benefits. Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem. This letter reports a dropout neuronal unit (1R1T-DNU) based on one memristor–one electrolyte-gated transistor with an ultralow energy consumption of 25 pJ/spike. A dropout neural network is constructed based on such a device and has been verified by MNIST dataset, demonstrating high recognition accuracies (&gt; 90%) within a large range of dropout probabilities up to 40%. The running time can be reduced by increasing dropout probability without a significant loss in accuracy. Our results indicate the great potential of introducing such 1R1T-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.